Digital breast tomosynthesis volumes provide more information than regular full field digital mammography (FFDM—Full Field Digital Mammography) images for early detection of abnormalities and cancer. Unfortunately, the reading time and therefore the cost of examination increases more than two-fold with digital breast tomosynthesis imaging compared to FFDM methods.
In conventional projection mammography it is possible that certain anatomical structures (for example calcium deposits or certain opacities) are not spotted or represented on the respective image, because superimposed structures disturb the visibility of underlying structures of the breast and in the end may lead to a falsely positive interpretation of the image.
In order to overcome the limitations of conventional mammography, digital breast tomosynthesis methods have been developed, which acquire several projections of an object (the breast) at different angles and thereafter reconstruct the three-dimensional distribution of the detected grey values in a detector by means of a tomography reconstruction algorithm. With digital breast tomosynthesis it is possible to detect any lesion that might have been masked during the superimposition of the tissues that takes place during a classic mammography projection acquisition. In digital breast tomosynthesis the breast is imaged under compression. A sequence of projection views is acquired by the digital detector as the X-ray source is rotated to different angular positions about a fulcrum over a finite angular range. Anatomical structures or objects at different heights (or depths in the breast) are projected differently at different angles. The subsequent image reconstruction algorithm leads to a stack or a slab of slice images of the different depth layers parallel to the detector surface. This technique enables the physician during diagnosis to “browse through” the interior of the female breast without obstruction by surrounding superimposed tissue.
Further details and principles of digital breast tomosynthesis and respective apparatuses are disclosed in “Digital breast tomosynthesis using an amorphous selenium flat panel detector”, M. Bissonnette et al., SPIE Vol. 5745, page 529 ff. For further information relating to reconstruction algorithms, particularly to filtered backprojection reconstruction algorithms it is referred to: “Optimizing filtered backprojection reconstruction for a breast tomosynthesis prototype device”, T. Mertelmeier et al. in: SPIE 6142 (2006). The full content of these papers is incorporated here by reference.
A major drawback of tomosynthesis systems, however, is to be seen in that, typically, the digital volume contains 50 to 80 slices. Thus, the volume to be loaded, processed and stored is high.
One of the usual ways of reducing the amount of data for read and for storage in regular computer tomography is the reconstruction of the volume in thick slices. While the modern computertomographs are capable of producing images of less than 0.5 mm slice thickness (for example in thoracic or in abdominal images), radiologists often read and analyze three-dimensional images reconstructed as thick slices or thick slabs (for example 2 to 5 mm).
However, a major drawback of state of the art reconstruction methods is that the diagnostically relevant information may be easily overlooked during diagnosis, because the diagnostic relevant regions are not uniformly distributed throughout the volume. Some slices could be combined into 5 mm slabs without any loss of diagnostically relevant image features, while in other areas, where the probability of finding a lesion or an anatomical abnormality is high, a corresponding higher resolution is required for an accurate diagnosis. Thus, known conventional methods, which are based on reconstructing the volume in slabs with fixed thickness and with one single reconstruction algorithm for the whole volume, are not best suited. On the one hand, imaging the entire organ with very high resolution is not always possible, because of the storage space and reading time limitations. On the other hand, reconstructing only the relevant sub-volume with high resolution and not visualizing the surrounding areas of tissue at all does not provide enough diagnostic context.
Therefore, there is a need to provide a reconstruction method and system which considers non-uniformly distribution of lesions in the volume and reconstruction methods, considering these distribution differences and taking into account that different reconstruction algorithms may be applied to different anatomical structures (lesions) in the same volume to be examined. Moreover, there is a need for a reconstruction algorithm which processes slabs with variable slice thickness and with variable resolution within each of the slices.